A Parallel Hybrid Technique for Multi-Noise Removal from Grayscale Medical Images
This work addresses noise removal in medical imaging for clinical purposes, but it is incremental as it applies existing filters with parallelization.
The paper tackles the problem of removing multiple noise types from large grayscale medical images by proposing a parallel hybrid filter combining adaptive median and Wiener filters, resulting in measured speed-up and efficiency improvements when tested on a 2.5 MB image with partitions of 2, 4, and 8.
Medical imaging is the technique used to create images of the human body or parts of it for clinical purposes. Medical images always have large sizes and they are commonly corrupted by single or multiple noise type at the same time, due to various reasons, these two reasons are the triggers for moving toward parallel image processing to find alternatives of image de-noising techniques. This paper presents a parallel hybrid filter implementation for gray scale medical image de-noising. The hybridization is between adaptive median and wiener filters. Parallelization is implemented on the adaptive median filter to overcome the latency of neighborhood operation, parfor implicit parallelism powered by MatLab 2013a is used. The implementation is tested on an image of 2.5 MB size, which is divided into 2, 4 and 8 partitions; a comparison between the proposed implementation and sequential implementation is given, in terms of time. Thus, each case has the best time when assigned to number of threads equal to the number of its partitions. Moreover, Speed up and efficiency are calculated for the algorithm and they show a measured enhancement.